This report covers the survey about attitudes collected by Richard Childers, MD and Joel Schofer, MD.

1 Summary

1.1 Notes

  1. The current report covers 951 responses.
  2. We excluded 17 cases because their orders preceded the year 2012 and 26 cases because the year_executed_order value was missing.

1.2 Unanswered Questions

1.3 Answered Questions

2 Histograms

2.1 Univariate

2.2 Frequency: homestead_length_in_years by officer_rank

2.3 Frequency: homestead_length_in_years by specialty_type

Frequency: homestead_problem by officer_rank ———————————–

2.4 Frequency: homestead_problem by specialty_type

2.5 Multivariate

3 Relationships between Outcomes

satisfaction rank transparency rank favoritism rank assignment current choice
satisfaction_rank 1.000 0.771 0.486 -0.519
transparency_rank 0.771 1.000 0.488 -0.405
favoritism_rank 0.486 0.488 1.000 -0.325
assignment_current_choice -0.519 -0.405 -0.325 1.000

4 Analyses - 1 Predictor

4.1 By Rank

4.1.1 satisfaction_rank


Data: ds
Formula: ~ satisfaction_rank 1 + officer_rate_f
term estimate std.error statistic p.value
(Intercept) 3.1361502 0.0903269 34.720017 0.00e+00
officer_rate_f4 0.4547589 0.1158670 3.924834 9.37e-05
officer_rate_f5 0.7953566 0.1268635 6.269388 0.00e+00
officer_rate_f6 0.9934794 0.1557247 6.379715 0.00e+00
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0619066 0.0586569 1.318277 19.04967 0 4 -1472.875 2955.751 2979.593 1504.982 866

4.1.2 transparency_rank


Data: ds
Formula: ~ transparency_rank 1 + officer_rate_f
term estimate std.error statistic p.value
(Intercept) 2.9018692 0.0923961 31.406827 0e+00
officer_rate_f4 0.5921428 0.1183507 5.003290 7e-07
officer_rate_f5 0.8972176 0.1299199 6.905929 0e+00
officer_rate_f6 1.0425753 0.1595401 6.534880 0e+00
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0685919 0.0653839 1.351639 21.38109 0 4 -1503.22 3016.439 3040.31 1591.254 871

4.1.3 favoritism_rank


Data: ds
Formula: ~ favoritism_rank 1 + officer_rate_f
term estimate std.error statistic p.value
(Intercept) 3.1216216 0.1092427 28.575103 0.0000000
officer_rate_f4 0.2028817 0.1333507 1.521415 0.1285723
officer_rate_f5 0.2581861 0.1429176 1.806538 0.0712303
officer_rate_f6 0.2974260 0.1695735 1.753965 0.0798401
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.005615 0.0016846 1.328995 1.428604 0.2331066 4 -1297.66 2605.319 2628.505 1340.567 759

4.1.4 assignment_current_choice


Data: ds
Formula: ~ assignment_current_choice 1 + officer_rate_f
term estimate std.error statistic p.value
(Intercept) 1.8177083 0.0808428 22.484485 0.0000000
officer_rate_f4 -0.1670234 0.1040813 -1.604740 0.1089568
officer_rate_f5 -0.4257485 0.1133191 -3.757077 0.0001848
officer_rate_f6 -0.4136679 0.1386022 -2.984570 0.0029287
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0222757 0.0185056 1.120191 5.908457 0.0005478 4 -1196.361 2402.722 2426.031 976.2552 778

4.2 By Specialty Type

4.2.1 satisfaction_rank


Data: [ ds ds$specialty_type != “unknown”
Formula: ~ satisfaction_rank 1 + specialty_type
term estimate std.error statistic p.value
(Intercept) 3.9172414 0.0625729 62.602823 0.0000000
specialty_typesurgical -0.2326776 0.1238796 -1.878257 0.0606834
specialty_typefamily -0.5635828 0.1195853 -4.712809 0.0000028
specialty_typeoperational -1.2544507 0.1540125 -8.145121 0.0000000
specialty_typeresident -0.4020899 0.2356418 -1.706360 0.0883012
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0811978 0.0769342 1.305062 19.04449 0 5 -1458.551 2929.103 2957.693 1468.146 862

4.2.2 transparency_rank


Data: [ ds ds$specialty_type != “unknown”
Formula: ~ transparency_rank 1 + specialty_type
term estimate std.error statistic p.value
(Intercept) 3.7214612 0.0646380 57.5738850 0.0000000
specialty_typesurgical -0.0888081 0.1289458 -0.6887244 0.4911810
specialty_typefamily -0.4850976 0.1235676 -3.9257663 0.0000933
specialty_typeoperational -1.1051821 0.1595528 -6.9267494 0.0000000
specialty_typeresident -0.5825723 0.2345448 -2.4838419 0.0131853
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0632343 0.0589124 1.352773 14.63123 0 5 -1498.288 3008.576 3037.201 1586.606 867

4.2.3 favoritism_rank


Data: [ ds ds$specialty_type != “unknown”
Formula: ~ favoritism_rank 1 + specialty_type
term estimate std.error statistic p.value
(Intercept) 3.4692308 0.0662709 52.3492083 0.0000000
specialty_typesurgical -0.1358974 0.1296285 -1.0483608 0.2948074
specialty_typefamily -0.3889388 0.1299775 -2.9923554 0.0028584
specialty_typeoperational -0.7633484 0.1719893 -4.4383491 0.0000104
specialty_typeresident 0.1021978 0.2560544 0.3991254 0.6899135
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0328075 0.0276901 1.308746 6.410944 4.48e-05 5 -1282.066 2576.132 2603.939 1294.889 756

4.2.4 assignment_current_choice


Data: [ ds ds$specialty_type != “unknown”
Formula: ~ assignment_current_choice 1 + specialty_type
term estimate std.error statistic p.value
(Intercept) 1.4912281 0.0561207 26.5718057 0.0000000
specialty_typesurgical 0.3361101 0.1104096 3.0442111 0.0024118
specialty_typefamily 0.0957285 0.1107059 0.8647099 0.3874656
specialty_typeoperational 0.4933873 0.1499427 3.2905058 0.0010453
specialty_typeresident -0.2091768 0.1880736 -1.1122073 0.2663939
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0255001 0.0204704 1.12101 5.069921 0.0004894 5 -1193.363 2398.727 2426.683 973.914 775

4.3 By Bonus Pay

4.3.1 satisfaction_rank


Data: ds
Formula: ~ satisfaction_rank 1 + bonus_pay_cut4
term estimate std.error statistic p.value
(Intercept) 2.8411215 0.1285699 22.097867 0e+00
bonus_pay_cut4$20-24k 0.8846169 0.1423437 6.214653 0e+00
bonus_pay_cut4$24-32k 0.9289934 0.1633872 5.685841 0e+00
bonus_pay_cut432k+ 0.9110153 0.1778978 5.121004 4e-07
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0472242 0.0439312 1.329938 14.34077 0 4 -1483.945 2977.89 3001.744 1535.262 868

4.3.2 transparency_rank


Data: ds
Formula: ~ transparency_rank 1 + bonus_pay_cut4
term estimate std.error statistic p.value
(Intercept) 2.6363636 0.1296906 20.328104 0e+00
bonus_pay_cut4$20-24k 0.9138456 0.1438411 6.353158 0e+00
bonus_pay_cut4$24-32k 1.1324225 0.1658741 6.827000 0e+00
bonus_pay_cut432k+ 0.9153605 0.1810230 5.056599 5e-07
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0559187 0.0526745 1.360206 17.23618 0 4 -1512.202 3034.403 3058.286 1615.191 873

4.3.3 favoritism_rank


Data: ds
Formula: ~ favoritism_rank 1 + bonus_pay_cut4
term estimate std.error statistic p.value
(Intercept) 3.0119048 0.1448614 20.791630 0.0000000
bonus_pay_cut4$20-24k 0.3295587 0.1590099 2.072567 0.0385488
bonus_pay_cut4$24-32k 0.4111722 0.1796785 2.288378 0.0223888
bonus_pay_cut432k+ 0.2489648 0.1905594 1.306495 0.1917791
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0075041 0.0035915 1.327677 1.917942 0.1251881 4 -1300.307 2610.614 2633.814 1341.434 761

4.3.4 assignment_current_choice


Data: ds
Formula: ~ assignment_current_choice 1 + bonus_pay_cut4
term estimate std.error statistic p.value
(Intercept) 1.7978723 0.1164006 15.445565 0.0000000
bonus_pay_cut4$20-24k -0.2789150 0.1287134 -2.166946 0.0305410
bonus_pay_cut4$24-32k -0.1312057 0.1468307 -0.893585 0.3718196
bonus_pay_cut432k+ -0.1740191 0.1588510 -1.095487 0.2736415
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0072089 0.0033904 1.128545 1.887914 0.1301122 4 -1205.251 2420.503 2443.825 993.4192 780

4.4 By Assignment Current Choice

4.4.1 satisfaction_rank


Data: ds
Formula: ~ satisfaction_rank 1 + assignment_current_choice
term estimate std.error statistic p.value
(Intercept) 4.7192430 0.0674241 69.99342 0
assignment_current_choice -0.5721764 0.0342102 -16.72533 0
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.2693047 0.268342 1.074932 279.7366 0 2 -1133.799 2273.598 2287.502 877.0091 759

4.4.2 transparency_rank


Data: ds
Formula: ~ transparency_rank 1 + assignment_current_choice
term estimate std.error statistic p.value
(Intercept) 4.3945478 0.0758513 57.93639 0
assignment_current_choice -0.4738032 0.0387336 -12.23236 0
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.1643153 0.1632172 1.209629 149.6306 0 2 -1226.858 2459.717 2473.629 1113.498 761

4.4.3 favoritism_rank


Data: ds
Formula: ~ favoritism_rank 1 + assignment_current_choice
term estimate std.error statistic p.value
(Intercept) 3.9860828 0.0822266 48.476775 0
assignment_current_choice -0.3706206 0.0416331 -8.902061 0
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.1053467 0.1040173 1.232284 79.24669 0 2 -1097.769 2201.538 2215.082 1021.967 673

4.5 By Year

4.5.1 satisfaction_rank

4.6 By Survey Lag

4.6.1 satisfaction_rank

4.7 By Manning Proportion

4.7.1 manning_proportion

4.8 By Crtical War

4.9 By Billet Current


Data: ds
Formula: ~ satisfaction_rank 1 + billet_current
term estimate std.error statistic p.value
(Intercept) 3.8565121 0.0617313 62.4725615 0.0000000
billet_currentGME -0.0474212 0.1396572 -0.3395546 0.7342744
billet_currentNon-Operational/Non-Clinical -0.2106788 0.1994360 -1.0563728 0.2910929
billet_currentOCONUS MTF -0.4465121 0.1451670 -3.0758512 0.0021651
billet_currentCONUS Operational -0.7404407 0.1386502 -5.3403496 0.0000001
billet_currentOCONUS Operational -1.4065121 0.2167199 -6.4899990 0.0000000
billet_currentOther 0.1434879 0.4422880 0.3244218 0.7456972
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0733123 0.0668844 1.313876 11.40534 0 7 -1471.84 2959.681 2997.847 1493.224 865

4.10 By Geographic Preference


Data: ds
Formula: ~ satisfaction_rank 1 + geographic_preference
term estimate std.error statistic p.value
(Intercept) 3.4523810 0.1206561 28.6133918 0.0000000
geographic_preferenceMiddle East or Africa -0.8523810 0.6175897 -1.3801736 0.1678910
geographic_preferenceNational Capital Region 0.2765910 0.1780473 1.5534691 0.1206782
geographic_preferenceNortheast -0.2349896 0.3070991 -0.7651915 0.4443670
geographic_preferencePacific (Hawaii, Guam, Japan) 0.2800134 0.2009802 1.3932387 0.1639068
geographic_preferencePacific Northwest -0.0157612 0.2009802 -0.0784218 0.9375107
geographic_preferenceSoutheast (North Carolina, Florida) 0.1309524 0.1834806 0.7137123 0.4755983
geographic_preferenceSouthern California 0.2957393 0.1464710 2.0190986 0.0437861
geographic_preferenceTidewater Region (Portsmouth, Norfolk) 0.3637110 0.1887902 1.9265350 0.0543661
geographic_preferenceUnknown -0.3523810 0.3259949 -1.0809401 0.2800261
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0187382 0.008493 1.354362 1.828979 0.0595147 10 -1496.789 3015.579 3068.058 1581.163 862

5 Analyses - 2 Predictors

5.1 By Rank and Specialty Type

5.1.1 satisfaction_rank


Data: [ ds ds$specialty_type != “unknown”
Formula: ~ satisfaction_rank 1 + officer_rate_f * specialty_type
term estimate std.error statistic p.value
(Intercept) 3.7794118 0.1553781 24.3239628 0.0000000
officer_rate_f4 -0.0092968 0.1832411 -0.0507355 0.9595483
officer_rate_f5 0.2560492 0.1891705 1.3535366 0.1762449
officer_rate_f6 0.5539216 0.2373440 2.3338342 0.0198372
specialty_typesurgical -0.1127451 0.3654966 -0.3084710 0.7577997
specialty_typefamily -0.9315857 0.2446039 -3.8085478 0.0001499
specialty_typeoperational -1.3667134 0.2240553 -6.0998925 0.0000000
specialty_typeresident -0.2294118 0.3259239 -0.7038814 0.4816999
officer_rate_f4:specialty_typesurgical -0.2384509 0.4064573 -0.5866567 0.5575904
officer_rate_f5:specialty_typesurgical 0.0516431 0.4328109 0.1193202 0.9050499
officer_rate_f6:specialty_typesurgical -0.1253501 0.4939158 -0.2537885 0.7997205
officer_rate_f4:specialty_typefamily 0.6244337 0.3157016 1.9779237 0.0482607
officer_rate_f5:specialty_typefamily 0.3285571 0.3403586 0.9653265 0.3346565
officer_rate_f6:specialty_typefamily 0.4871412 0.3909269 1.2461183 0.2130651
officer_rate_f4:specialty_typeoperational 0.6680270 0.4205932 1.5882972 0.1125919
officer_rate_f5:specialty_typeoperational 2.3312524 1.3051914 1.7861383 0.0744341
officer_rate_f6:specialty_typeoperational 0.6583800 0.5362841 1.2276701 0.2199115
officer_rate_f4:specialty_typeresident -0.0791647 0.4918785 -0.1609436 0.8721761
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.1249664 0.1074244 1.281281 7.123859 0 18 -1434.353 2906.706 2997.22 1392.145 848

Data: [ ds ds$specialty_type != “unknown”
Formula: ~ satisfaction_rank 1 + officer_rate_f + specialty_type
term estimate std.error statistic p.value
(Intercept) 3.6060419 0.1138533 31.6726901 0.0000000
officer_rate_f4 0.1894040 0.1227398 1.5431344 0.1231669
officer_rate_f5 0.4624532 0.1364136 3.3900819 0.0007306
officer_rate_f6 0.7806986 0.1587342 4.9182747 0.0000010
specialty_typesurgical -0.2466207 0.1222314 -2.0176535 0.0439382
specialty_typefamily -0.5476117 0.1188297 -4.6083747 0.0000047
specialty_typeoperational -1.0520849 0.1656575 -6.3509630 0.0000000
specialty_typeresident -0.1655041 0.2383040 -0.6945081 0.4875516
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.113058 0.1058219 1.282431 15.62411 0 8 -1440.206 2898.412 2941.287 1411.091 858

TODO: examine if the interaction term significantly improves fit.### transparency_rank


Data: ds
Formula: ~ transparency_rank 1 + specialty_type
term estimate std.error statistic p.value
(Intercept) 3.7214612 0.0648138 57.4177578 0.0000000
specialty_typesurgical -0.0888081 0.1292964 -0.6868568 0.4923558
specialty_typefamily -0.4850976 0.1239036 -3.9151205 0.0000974
specialty_typeoperational -1.1051821 0.1599866 -6.9079656 0.0000000
specialty_typeresident -0.5825723 0.2351826 -2.4771063 0.0134340
specialty_typeunknown -0.7214612 0.6100763 -1.1825754 0.2373002
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0632744 0.0578971 1.356452 11.76695 0 6 -1508.772 3031.543 3064.979 1602.606 871

5.1.2 favoritism_rank


Data: ds
Formula: ~ favoritism_rank 1 + specialty_type
term estimate std.error statistic p.value
(Intercept) 3.4692308 0.0664647 52.1966278 0.0000000
specialty_typesurgical -0.1358974 0.1300074 -1.0453052 0.2962147
specialty_typefamily -0.3889388 0.1303574 -2.9836337 0.0029398
specialty_typeoperational -0.7633484 0.1724920 -4.4254128 0.0000110
specialty_typeresident 0.1021978 0.2568029 0.3979621 0.6907700
specialty_typeunknown -0.2192308 0.6596428 -0.3323477 0.7397185
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0325083 0.0261348 1.312572 5.100571 0.0001313 6 -1290.547 2595.095 2627.574 1307.639 759

5.1.3 assignment_current_choice


Data: ds
Formula: ~ assignment_current_choice 1 + specialty_type
term estimate std.error statistic p.value
(Intercept) 1.4912281 0.0560339 26.6129401 0.0000000
specialty_typesurgical 0.3361101 0.1102389 3.0489237 0.0023744
specialty_typefamily 0.0957285 0.1105347 0.8660485 0.3867305
specialty_typeoperational 0.4933873 0.1497109 3.2955997 0.0010266
specialty_typeresident -0.2091768 0.1877829 -1.1139290 0.2656536
specialty_typeunknown -0.2412281 0.5624368 -0.4288981 0.6681161
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0259522 0.0196923 1.119277 4.145762 0.0010079 6 -1197.78 2409.56 2442.211 974.664 778

5.2 By Rank and Assignment Current Choice

5.2.1 satisfaction_rank


Data: ds
Formula: ~ satisfaction_rank 1 + officer_rate_f + assignment_current_choice
term estimate std.error statistic p.value
(Intercept) 4.2860493 0.0996705 43.002177 0.0000000
officer_rate_f4 0.3900905 0.0997785 3.909563 0.0001008
officer_rate_f5 0.5345137 0.1088637 4.909937 0.0000011
officer_rate_f6 0.7636977 0.1325867 5.759988 0.0000000
assignment_current_choice -0.5388720 0.0338151 -15.935829 0.0000000
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.3052065 0.3015205 1.048202 82.80361 0 5 -1110.197 2232.395 2260.187 828.4411 754

Data: ds
Formula: ~ satisfaction_rank 1 + officer_rate_f * assignment_current_choice
term estimate std.error statistic p.value
(Intercept) 4.2547092 0.1334327 31.8865648 0.0000000
officer_rate_f4 0.4158921 0.1724797 2.4112525 0.0161369
officer_rate_f5 0.6520120 0.1918718 3.3981645 0.0007142
officer_rate_f6 0.7511815 0.2254399 3.3320701 0.0009042
assignment_current_choice -0.5218961 0.0587130 -8.8889425 0.0000000
officer_rate_f4:assignment_current_choice -0.0136552 0.0796330 -0.1714763 0.8638954
officer_rate_f5:assignment_current_choice -0.0789217 0.1018947 -0.7745421 0.4388541
officer_rate_f6:assignment_current_choice 0.0140756 0.1196004 0.1176887 0.9063458
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.3058807 0.2994109 1.049784 47.27809 0 8 -1109.829 2237.658 2279.346 827.6371 751

Analysis of Variance Table

Model 1: satisfaction_rank ~ 1 + officer_rate_f + assignment_current_choice Model 2: satisfaction_rank ~ 1 + officer_rate_f * assignment_current_choice Res.Df RSS Df Sum of Sq F Pr(>F) 1 754 828.44
2 751 827.64 3 0.80396 0.2432 0.8662 ### transparency_rank


Data: ds
Formula: ~ transparency_rank 1 + officer_rate_f * assignment_current_choice
term estimate std.error statistic p.value
(Intercept) 3.8638632 0.1499297 25.7711726 0.0000000
officer_rate_f4 0.4825983 0.1937144 2.4912871 0.0129424
officer_rate_f5 0.6305539 0.2161270 2.9175158 0.0036333
officer_rate_f6 1.0465560 0.2579506 4.0571960 0.0000548
assignment_current_choice -0.4305167 0.0660775 -6.5153323 0.0000000
officer_rate_f4:assignment_current_choice 0.0217793 0.0896179 0.2430244 0.8080527
officer_rate_f5:assignment_current_choice 0.0254252 0.1147065 0.2216542 0.8246432
officer_rate_f6:assignment_current_choice -0.2130761 0.1435839 -1.4839828 0.1382319
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.2076297 0.2002637 1.18273 28.1876 0 8 -1203.506 2425.012 2466.724 1053.334 753

5.2.2 favoritism_rank


Data: ds
Formula: ~ favoritism_rank 1 + officer_rate_f * assignment_current_choice
term estimate std.error statistic p.value
(Intercept) 3.8687050 0.1889326 20.4766424 0.0000000
officer_rate_f4 0.1174241 0.2324391 0.5051821 0.6135983
officer_rate_f5 0.0937283 0.2502167 0.3745885 0.7080859
officer_rate_f6 0.3797836 0.2860813 1.3275372 0.1847866
assignment_current_choice -0.3171891 0.0798386 -3.9728796 0.0000788
officer_rate_f4:assignment_current_choice -0.0355036 0.1036411 -0.3425626 0.7320358
officer_rate_f5:assignment_current_choice -0.0549110 0.1273840 -0.4310667 0.6665595
officer_rate_f6:assignment_current_choice -0.2321125 0.1465992 -1.5833140 0.1138253
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.108222 0.0988349 1.235585 11.52876 0 8 -1093.291 2204.582 2245.188 1015.236 665

5.3 By Rank and Bonus Pay

5.3.1 satisfaction_rank


Data: ds
Formula: ~ satisfaction_rank 1 + officer_rate_f + bonus_pay
term estimate std.error statistic p.value
(Intercept) 2.9244122 0.1132714 25.817747 0.0000000
officer_rate_f4 0.2934115 0.1267040 2.315724 0.0208064
officer_rate_f5 0.6032909 0.1408814 4.282261 0.0000206
officer_rate_f6 0.8478220 0.1620620 5.231467 0.0000002
bonus_pay 0.0000158 0.0000051 3.072155 0.0021917
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0720318 0.0677406 1.311901 16.786 0 5 -1468.155 2948.309 2976.92 1488.738 865

Data: ds
Formula: ~ satisfaction_rank 1 + officer_rate_f * bonus_pay
term estimate std.error statistic p.value
(Intercept) 2.6738295 0.1412220 18.933520 0.0000000
officer_rate_f4 0.9876528 0.2498733 3.952615 0.0000836
officer_rate_f5 0.8154021 0.3806246 2.142274 0.0324510
officer_rate_f6 1.1783927 0.4842708 2.433334 0.0151628
bonus_pay 0.0000345 0.0000081 4.232110 0.0000256
officer_rate_f4:bonus_pay -0.0000374 0.0000115 -3.245845 0.0012162
officer_rate_f5:bonus_pay -0.0000172 0.0000157 -1.096670 0.2730919
officer_rate_f6:bonus_pay -0.0000222 0.0000213 -1.042449 0.2974956
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0832837 0.0758394 1.30619 11.18754 0 8 -1462.848 2943.696 2986.612 1470.687 862

Analysis of Variance Table

Model 1: satisfaction_rank ~ 1 + officer_rate_f + bonus_pay Model 2: satisfaction_rank ~ 1 + officer_rate_f * bonus_pay Res.Df RSS Df Sum of Sq F Pr(>F) 1 865 1488.7
2 862 1470.7 3 18.052 3.5268 0.01461 ### transparency_rank


Data: ds
Formula: ~ transparency_rank 1 + officer_rate_f * bonus_pay
term estimate std.error statistic p.value
(Intercept) 2.4796068 0.1432849 17.3054341 0.0000000
officer_rate_f4 1.0075219 0.2531992 3.9791673 0.0000749
officer_rate_f5 0.8584585 0.3901640 2.2002504 0.0280522
officer_rate_f6 0.8102031 0.5006221 1.6183927 0.1059417
bonus_pay 0.0000318 0.0000083 3.8335875 0.0001354
officer_rate_f4:bonus_pay -0.0000315 0.0000117 -2.6878622 0.0073290
officer_rate_f5:bonus_pay -0.0000138 0.0000161 -0.8554228 0.3925535
officer_rate_f6:bonus_pay -0.0000028 0.0000221 -0.1257225 0.8999807
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0879753 0.0806117 1.340582 11.94744 0 8 -1494.019 3006.038 3049.006 1558.138 867

5.3.2 favoritism_rank


Data: ds
Formula: ~ favoritism_rank 1 + officer_rate_f * bonus_pay
term estimate std.error statistic p.value
(Intercept) 2.8463186 0.1678880 16.9536708 0.0000000
officer_rate_f4 0.6600801 0.2736657 2.4119942 0.0161031
officer_rate_f5 0.2419408 0.4029886 0.6003664 0.5484422
officer_rate_f6 0.6359086 0.5053006 1.2584759 0.2086087
bonus_pay 0.0000211 0.0000098 2.1572493 0.0313011
officer_rate_f4:bonus_pay -0.0000288 0.0000130 -2.2189548 0.0267864
officer_rate_f5:bonus_pay -0.0000098 0.0000169 -0.5808773 0.5614965
officer_rate_f6:bonus_pay -0.0000239 0.0000225 -1.0656273 0.2869328
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.01366 0.0045151 1.327109 1.493731 0.1660622 8 -1294.56 2607.121 2648.856 1329.721 755

5.4 By Billet Current and Critical War

5.4.1 satisfaction_rank


Data: ds
Formula: ~ satisfaction_rank 1 + billet_current + critical_war
term estimate std.error statistic p.value
(Intercept) 3.8482807 0.1221609 31.5017379 0.0000000
billet_currentGME -0.0470058 0.1398387 -0.3361429 0.7368447
billet_currentNon-Operational/Non-Clinical -0.2106988 0.1995509 -1.0558651 0.2913250
billet_currentOCONUS MTF -0.4462987 0.1452762 -3.0720701 0.0021924
billet_currentCONUS Operational -0.7404032 0.1387308 -5.3369783 0.0000001
billet_currentOCONUS Operational -1.4067334 0.2168630 -6.4867362 0.0000000
billet_currentOther 0.1420591 0.4429202 0.3207329 0.7484904
critical_warLow Deployer 0.0096602 0.1236892 0.0781008 0.9377659
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0733188 0.065811 1.314632 9.765642 0 8 -1471.837 2961.675 3004.612 1493.214 864

5.5 By Bonus_pay and Manning_proportion

5.5.1 satisfaction_rank


Data: ds
Formula: ~ satisfaction_rank 1 + manning_proportion_cut3 + bonus_pay_cut3
term estimate std.error statistic p.value
(Intercept) 2.4680836 0.1647969 14.976514 0.0000000
manning_proportion_cut3Balanced 0.3928698 0.1259855 3.118373 0.0018786
manning_proportion_cut3Over 0.4651373 0.1209922 3.844358 0.0001297
bonus_pay_cut3$20-24k 1.0172580 0.1493703 6.810311 0.0000000
bonus_pay_cut3$24k+ 0.8796862 0.1546106 5.689687 0.0000000
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0642006 0.0598832 1.318796 14.87015 0 5 -1476.106 2964.213 2992.838 1507.907 867

No interaction between manning_proportion_cut3 & bonus_pay_cut3Analysis of Variance Table

Model 1: satisfaction_rank ~ 1 + manning_proportion_cut3 * bonus_pay_cut3 Model 2: satisfaction_rank ~ 1 + manning_proportion_cut3 + bonus_pay_cut3 Res.Df RSS Df Sum of Sq F Pr(>F) 1 863 1499.7
2 867 1507.9 -4 -8.1779 1.1765 0.3197
Data: ds
Formula: ~ satisfaction_rank 1 + billet_current + critical_war
term estimate std.error statistic p.value
(Intercept) 3.8482807 0.1221609 31.5017379 0.0000000
billet_currentGME -0.0470058 0.1398387 -0.3361429 0.7368447
billet_currentNon-Operational/Non-Clinical -0.2106988 0.1995509 -1.0558651 0.2913250
billet_currentOCONUS MTF -0.4462987 0.1452762 -3.0720701 0.0021924
billet_currentCONUS Operational -0.7404032 0.1387308 -5.3369783 0.0000001
billet_currentOCONUS Operational -1.4067334 0.2168630 -6.4867362 0.0000000
billet_currentOther 0.1420591 0.4429202 0.3207329 0.7484904
critical_warLow Deployer 0.0096602 0.1236892 0.0781008 0.9377659
r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual
0.0733188 0.065811 1.314632 9.765642 0 8 -1471.837 2961.675 3004.612 1493.214 864

6 Session Information

For the sake of documentation and reproducibility, the current report was rendered in the following environment. Click the line below to expand.

Environment 

Session info --------------------------------------------------------------------------------------
 setting  value                       
 version  R version 3.4.4 (2018-03-15)
 system   x86_64, linux-gnu           
 ui       X11                         
 language (EN)                        
 collate  en_US.UTF-8                 
 tz       America/Chicago             
 date     2018-07-01                  
Packages ------------------------------------------------------------------------------------------
 package         * version     date       source                                  
 assertthat        0.2.0       2017-04-11 cran (@0.2.0)                           
 backports         1.1.2       2017-12-13 cran (@1.1.2)                           
 base            * 3.4.4       2018-04-21 local                                   
 bindr             0.1.1       2018-03-13 CRAN (R 3.4.3)                          
 bindrcpp        * 0.2.2       2018-03-29 CRAN (R 3.4.3)                          
 broom             0.4.4       2018-03-29 CRAN (R 3.4.3)                          
 colorspace        1.3-2       2016-12-14 CRAN (R 3.4.3)                          
 compiler          3.4.4       2018-04-21 local                                   
 corrplot          0.84        2017-10-16 CRAN (R 3.4.3)                          
 datasets        * 3.4.4       2018-04-21 local                                   
 devtools          1.13.5      2018-02-18 CRAN (R 3.4.3)                          
 digest            0.6.15      2018-01-28 CRAN (R 3.4.3)                          
 dplyr             0.7.5       2018-05-19 CRAN (R 3.4.4)                          
 evaluate          0.10.1      2017-06-24 CRAN (R 3.4.3)                          
 foreign           0.8-69      2017-06-21 CRAN (R 3.4.2)                          
 ggplot2         * 2.2.1       2016-12-30 CRAN (R 3.4.4)                          
 glue              1.2.0       2017-10-29 cran (@1.2.0)                           
 graphics        * 3.4.4       2018-04-21 local                                   
 grDevices       * 3.4.4       2018-04-21 local                                   
 grid              3.4.4       2018-04-21 local                                   
 gtable            0.2.0       2016-02-26 CRAN (R 3.4.3)                          
 highr             0.7         2018-06-09 CRAN (R 3.4.4)                          
 hms               0.4.2.9000  2018-05-26 Github (tidyverse/hms@14e74ab)          
 htmltools         0.3.6       2017-04-28 CRAN (R 3.4.3)                          
 httr              1.3.1       2017-08-20 CRAN (R 3.4.3)                          
 kableExtra        0.9.0       2018-05-21 CRAN (R 3.4.4)                          
 knitr           * 1.20        2018-02-20 CRAN (R 3.4.3)                          
 labeling          0.3         2014-08-23 CRAN (R 3.4.3)                          
 lattice           0.20-35     2017-03-25 CRAN (R 3.4.2)                          
 lazyeval          0.2.1       2017-10-29 CRAN (R 3.4.3)                          
 magrittr        * 1.5         2014-11-22 cran (@1.5)                             
 memoise           1.1.0       2017-04-21 CRAN (R 3.4.3)                          
 methods         * 3.4.4       2018-04-21 local                                   
 mnormt            1.5-5       2016-10-15 CRAN (R 3.4.3)                          
 munsell           0.5.0       2018-06-12 CRAN (R 3.4.4)                          
 nlme              3.1-137     2018-04-07 CRAN (R 3.4.2)                          
 parallel          3.4.4       2018-04-21 local                                   
 pillar            1.2.3       2018-05-25 CRAN (R 3.4.4)                          
 pkgconfig         2.0.1       2017-03-21 cran (@2.0.1)                           
 plyr              1.8.4       2016-06-08 CRAN (R 3.4.3)                          
 psych             1.8.4       2018-05-06 CRAN (R 3.4.4)                          
 purrr             0.2.5       2018-05-29 CRAN (R 3.4.4)                          
 R6                2.2.2       2017-06-17 CRAN (R 3.4.3)                          
 Rcpp              0.12.17     2018-05-18 CRAN (R 3.4.4)                          
 readr             1.2.0       2018-05-26 Github (tidyverse/readr@d6d622b)        
 reshape2          1.4.3       2017-12-11 CRAN (R 3.4.3)                          
 rlang             0.2.1       2018-05-30 CRAN (R 3.4.4)                          
 rmarkdown         1.10        2018-06-11 CRAN (R 3.4.4)                          
 rprojroot         1.3-2       2018-01-03 CRAN (R 3.4.3)                          
 rstudioapi        0.7         2017-09-07 CRAN (R 3.4.3)                          
 rvest             0.3.2       2016-06-17 CRAN (R 3.4.3)                          
 scales            0.5.0.9000  2018-03-29 Github (hadley/scales@d767915)          
 stats           * 3.4.4       2018-04-21 local                                   
 stringi           1.2.3       2018-06-12 CRAN (R 3.4.4)                          
 stringr           1.3.1       2018-05-10 CRAN (R 3.4.4)                          
 TabularManifest   0.1-16.9003 2018-03-29 Github (Melinae/TabularManifest@c2bdddb)
 tibble            1.4.2       2018-01-22 CRAN (R 3.4.3)                          
 tidyr             0.8.1       2018-05-18 CRAN (R 3.4.4)                          
 tidyselect        0.2.4       2018-02-26 CRAN (R 3.4.3)                          
 tools             3.4.4       2018-04-21 local                                   
 utils           * 3.4.4       2018-04-21 local                                   
 viridisLite       0.3.0       2018-02-01 CRAN (R 3.4.3)                          
 withr             2.1.2       2018-03-29 Github (jimhester/withr@79d7b0d)        
 xml2              1.2.0       2018-01-24 CRAN (R 3.4.3)                          
 yaml              2.1.19      2018-05-01 CRAN (R 3.4.4)

Report rendered by wibeasley at 2018-07-01, 01:05 -0500 in 39 seconds.